An Adaptive Alignment Algorithm for Quality-controlled Label-free LC-MS

Abstract

Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software. Molecular & Cellular Proteomics 12: 10.1074/mcp.O112.021907, 1407-1420, 2013. Shotgun liquid chromatography (LC)1 -MS/MS is widely used for quantitative proteomics research. There are several LC-MS/MS approaches that rely on chemical labels as internal standards, and additionally numerous label-free workflows exist. Of these options, label-free quantification has lately emerged as a viable approach because of its lack of limitations concerning the number and types of samples under investigation (1-3). Despite the advantages, there are a number of data analysis challenges (4) associated with the label-free methodology that may unnecessarily increase technical variation. This has led to an uncertainty toward the usage of the label-free workflows, to much extent because of difficulties in assessing how well the data analysis works for the experiment at hand. There is a large amount of software available for label-free data processing and recent studies have shown that the results of data analysis vary considerably with the choice of software (5-7).Two major types of label-free quantification exist; spectral count and precursor-based. The former is based on the number of times a peptide has been subjected to fragmentation using MS/MS. The counts for the peptide content of a protein are subsequently recalculated into a protein count, which is related to the protein abundance (3). Although straightforward, this quantification strategy has proven less accurate than the precursor-based method as the number of spectra necessary to quantify more subtle expression changes increases exponentially (8). As quantification is coupled to identification, the dependence on the semirandom MS/MS sampling process leads to variability in protein abundance (9). The quantification coverage is further limited as the commonly employed dynamic exclusion for increasing the number of identifications introduces saturation effects into the analysis (8). In contrast, label-free quantification using precursor intensities shows great potential for large-scale proteomic analyses (1-3, 10 -13).Data from a label-free sample consists of LC-MS files that can be visualized individually as t...

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“…25 MS/MS identification was performed by combining the peptide-spectrum level search results from X!Tandem and Mascot. Search settings were 7 ppm precursor tolerance and 0.5 Da fragment tolerance, with fixed carbamidomethylation of cysteins and variable oxidation of methionines.…”

Middle-Down and Chemical Proteomic Approaches to Reveal Histone H4 Modification Dynamics in Cell Cycle

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Proteomic Analysis of Phytophthora infestans Reveals the Importance of Cell Wall Proteins in Pathogenicity

“…The feature detection step was performed on mzML files using msInspect (25) and subsequent feature matching and alignment between LC-MS/MS runs with a previously described workflow (26). The quantitative, un-normalized data for all peptides used for the quantitative analysis is shown in supplemental Table S1.…”